Description
TensorFlow Lite: Deploy AI on Mobile, Embedded, and IoT Devices
Your model is trained—but can it run on a phone, Raspberry Pi, or microcontroller? **TensorFlow Lite** brings deep learning to the edge, enabling real-time inference with low latency and minimal power. This course teaches you to **convert, optimize, and deploy** models for Android, iOS, and microcontrollers.
What You’ll Build
- A mobile image classifier for Android using TFLite
- A keyword spotter for embedded devices (like “Hey Google”)
- A real-time pose estimator for iOS with Core ML conversion
- A microcontroller-based gesture recognizer with TFLite Micro
Key Techniques Covered
- Model conversion—from Keras, TensorFlow, or PyTorch to TFLite
- Quantization—post-training and quantization-aware training (QAT)
- Optimization—pruning, clustering for smaller, faster models
- Deployment—Android (Java/Kotlin), iOS (Swift), and C++ for micro
- Benchmarking—latency, memory, and accuracy trade-offs
Why Edge AI Matters
- Privacy—data never leaves the device
- Low latency—real-time response without network round trips
- Offline capability—works without internet
- Cost savings—reduce cloud inference bills
Who Is This For?
- Mobile developers adding AI to apps
- Embedded engineers building smart devices
- ML engineers optimizing models for production
- Startups targeting low-bandwidth markets (like Nigeria)
From Cloud to Edge—Seamlessly
You’ll learn the **exact workflow** used by Google, Tesla, and startups to bring AI out of the data center and into users’ hands.
Ready to deploy AI anywhere? Enroll now.
